MLOPS

Automating AI/ML model deployment and Setting up monitoring for the ML pipeline
- Automating CI/CD pipelines to account for data, code, and model changes
- Programming, working knowledge of machine learning algorithms and frameworks, and domain knowledge
- Querying and working with databases, testing ML models, Git and version control, frameworks like Flask, FastAPI
- Proficiency in tools such as Docker and Kubernetes 
- Familiarity with experiment tracking frameworks such as MLflow
- Setting up and automating data pipelines using tools such as Airflow, Kafka amd Rabbitmq
- Providing best practices and executing POC for automated and efficient model operations at scale.
- Good to have hands-on experience using large foundation models (e.g. LLMs) and associated tool chains (e.g. langchain) and APIs to build applications, tools and workflows for production.